Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
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Abstract (arXiv)
We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to out-of-the-box Surprise library, Auto-Surprise performs better when evaluated with MovieLens, Book Crossing and Jester Datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Aug 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 3; 2026: 1.
7 papers.
- 2024Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performancecited by 4
- 2022On the Generalizability and Predictability of Recommender Systemscited by 3
- 2024The Potential of AutoML for Recommender Systemscited by 3
- 2025Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristicscited by 1
- 2025From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labscited by 1
- 2025Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworkscited by 0
- 2026AutoRecLab: Describe the Experiment, Get the Code!cited by 0
- 2026AutoRecLab: Describe the Experiment, Get the Code!18 Sep 2026
- 2025From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs20 Oct 2025
- 2025Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics6 Aug 2025
- 2025Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks3 Jun 2025
- 2024Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance12 Oct 2024
- 2024The Potential of AutoML for Recommender Systems6 Feb 2024
- 2022On the Generalizability and Predictability of Recommender Systems23 Jun 2022